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Title: In silico de novo design of NNRTIs of HIV-1: Functional group based computational molecular modelling approach
Authors: Raghuvanshi, U
Sapre, N S
Keywords: Back Propagation Neural Networks (BPNN);De Novo Design;Molecular Modeling;Multiple Linear Regression (MLR);NNRTIs;Support Vector Machine (SVM)
Issue Date: Oct-2020
Publisher: NISCAIR-CSIR, India
Abstract: Seven novel lead compounds, acting as NNRTIs of HIV-1, are extracted from a database of, in silico de novo designed, 500 compounds. Functional group based computational molecular modelling techniques are used for such design of Acylthiocarbamate derivatives. Effect of structural characteristics on the antiviral activity of these derivatives has also been studied. Statistical regression techniques namely, Non-linear (Back Propagation Neural Network, Support Vector Machine) and linear (Multiple Linear) chemometric regression methods are used in developing the relationships of Kier-Hall Electrotopological State Indices (ERingA, EO8, EN9, EO14, ES16, EN17, EO19, ER, and ER1) with the HIV-1 antiviral activity. The relative potentials of these methods are also assessed and the results suggest that BPNN (r2 = 0.845, MSE = 0.142, q2 = 0.818) describes the relationship between the descriptors and antiviral activity in a relatively better manner than SVM-ε-radial (r2 = 0.844, MSE = 0.144, q2 = 0.807) and MLR (r2 = 0.836, MSE = 0.150, q2 = 0.805).
Page(s): 1484-1493
ISSN: 0975-0975(Online); 0376-4710(Print)
Appears in Collections:IJC-A Vol.59A(10) [October 2020]

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